⚽ FM'22 Players Wages Prediction ⚽
We predicted the wages of the FM'22 players using the data we obtained with selenium on the fminside.
Dataset Information
There are 4 different data in our project. Two of them are data of FM'22 players and clubs. The other two are data of FM'21 players and clubs. Our main data is the data of FM'22 players. The target variable may differ from project to project. However, we have estimated the salaries of the players in this project.
FM'22 Players Dataset
- The dataset has 53 columns and approximately 15k rows. Each row contains information about different players.
FM'22 Clubs Dataset
- The dataset has 18 columns and approximately 3k rows. Each row contains information about different clubs.
FM'21 Players Dataset
- The dataset has 53 columns and approximately 15k rows. Each row contains information about different players.
FM'21 Clubs Dataset
- The dataset has 18 columns and approximately 3k rows. Each row contains information about different clubs.
Requirements
There are some general library requirements for the Project. The general requirements are as follows.
- Numpy
- Pandas
- Scikit-learn
- Datetime
- Streamlit
- Selenium
For Visualization
- Matplotlib
- Seaborn
- Plotly
The library requirements specific to some methods are:
- CatBoost Model
- GradientBoosting Model
- XGBoost Model
- LightGBM Model
- Random Forest Model
Content
- Import Module and Data
- Exploratory Data Analysis
- Data Preprocessing
- Datasets Merge
- Filling the Missing Value
- Data Correction
- Outlier Values
- Correlation Hypothesis
- Data Visualization
- Future Engineering
- Creating a New Variables
- Encoding
- Scaling
- Building Models
- CatBoost Model
- GradientBoosting Model
- XGBoost Model
- LightGBM Model
- Random Forest Model
Our Website Preview
FM22-Players-Wage-Prediction-Website-Preview.mp4
Members
| Project Team |
|---|
| Muhammed Nafiz Canıtez |
| Abdulezel Bilgili |
| Ali Batuhan Öztürk |